title: Exploring the social determinants of mental health service use using intersectionality theory and CART analysis.
abstract: BACKGROUND
  Fewer than half of individuals with a mental disorder seek formal care in a given year. Much research has been conducted on the factors that influence service use in this population, but the methods generally used cannot easily identify the complex interactions that are thought to exist. In this paper, we examine predictors of subsequent service use among respondents to a population health survey who met criteria for a past-year mood, anxiety or substance-related disorder.
  
  
  METHODS
  To determine service use, we use an administrative database including all physician consultations in the period of interest. To identify predictors, we use classification tree (CART) analysis, a data mining technique with the ability to identify unsuspected interactions. We compare results to those from logistic regression models.
  
  
  RESULTS
  We identify 1213 individuals with past-year disorder. In the year after the survey, 24% (n=312) of these had a mental health-related physician consultation. Logistic regression revealed that age, sex and marital status predicted service use. CART analysis yielded a set of rules based on age, sex, marital status and income adequacy, with marital status playing a role among men and by income adequacy important among women. CART analysis proved moderately effective overall, with agreement of 60%, sensitivity of 82% and specificity of 53%.
  
  
  CONCLUSIONS
  Results highlight the potential of data-mining techniques to uncover complex interactions, and offer support to the view that the intersection of multiple statuses influence health and behaviour in ways that are difficult to identify with conventional statistics. The disadvantages of these methods are also discussed.
authors.affiliation: Departments of Family Medicine, Psychiatry & Behavioural Neurosciences, and Kinesiology, McMaster University, , Hamilton, Ontario, Canada.
authors.forename: John
authors.initials: J
authors.lastname: Cairney
authors.forename: Scott
authors.initials: S
authors.lastname: Veldhuizen
authors.forename: Simone
authors.initials: S
authors.lastname: Vigod
authors.forename: David L
authors.initials: DL
authors.lastname: Streiner
authors.forename: Terrance J
authors.initials: TJ
authors.lastname: Wade
authors.forename: Paul
authors.initials: P
authors.lastname: Kurdyak
mesh_terms: D000328:Adult; D002170:Canada / Q000453:epidemiology; D057225:Data Mining / Q000379:methods*; D005260:Female; D019538:Health Care Surveys; D006801:Humans; D008297:Male; D001523:Mental Disorders / Q000453:epidemiology / Q000628:therapy*; D008605:Mental Health Services / Q000706:statistics & numerical data*; D008875:Middle Aged; D008962:Models, Theoretical; D010342:Patient Acceptance of Health Care / Q000208:ethnology / Q000706:statistics & numerical data; D011204:Poverty Areas; D012044:Regression Analysis; D012424:Rural Population; D012923:Social Class; D064890:Social Determinants of Health; D055815:Young Adult
keywords: Access to Hlth Care; Mental Health; Modelling; Social Inequalities; Social Science